{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
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  "orig_nbformat": 4,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.0 64-bit"
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  "interpreter": {
   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Bird distributions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Visualize the dataset"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "birds = pd.read_csv('../../../data/birds.csv')\n",
    "birds.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                           Name          ScientificName  \\\n",
       "0  Black-bellied whistling-duck  Dendrocygna autumnalis   \n",
       "1        Fulvous whistling-duck     Dendrocygna bicolor   \n",
       "2                    Snow goose      Anser caerulescens   \n",
       "3                  Ross's goose            Anser rossii   \n",
       "4   Greater white-fronted goose         Anser albifrons   \n",
       "\n",
       "                Category         Order    Family        Genus  \\\n",
       "0  Ducks/Geese/Waterfowl  Anseriformes  Anatidae  Dendrocygna   \n",
       "1  Ducks/Geese/Waterfowl  Anseriformes  Anatidae  Dendrocygna   \n",
       "2  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
       "3  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
       "4  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
       "\n",
       "  ConservationStatus  MinLength  MaxLength  MinBodyMass  MaxBodyMass  \\\n",
       "0                 LC       47.0       56.0        652.0       1020.0   \n",
       "1                 LC       45.0       53.0        712.0       1050.0   \n",
       "2                 LC       64.0       79.0       2050.0       4050.0   \n",
       "3                 LC       57.3       64.0       1066.0       1567.0   \n",
       "4                 LC       64.0       81.0       1930.0       3310.0   \n",
       "\n",
       "   MinWingspan  MaxWingspan  \n",
       "0         76.0         94.0  \n",
       "1         85.0         93.0  \n",
       "2        135.0        165.0  \n",
       "3        113.0        116.0  \n",
       "4        130.0        165.0  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ScientificName</th>\n",
       "      <th>Category</th>\n",
       "      <th>Order</th>\n",
       "      <th>Family</th>\n",
       "      <th>Genus</th>\n",
       "      <th>ConservationStatus</th>\n",
       "      <th>MinLength</th>\n",
       "      <th>MaxLength</th>\n",
       "      <th>MinBodyMass</th>\n",
       "      <th>MaxBodyMass</th>\n",
       "      <th>MinWingspan</th>\n",
       "      <th>MaxWingspan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Black-bellied whistling-duck</td>\n",
       "      <td>Dendrocygna autumnalis</td>\n",
       "      <td>Ducks/Geese/Waterfowl</td>\n",
       "      <td>Anseriformes</td>\n",
       "      <td>Anatidae</td>\n",
       "      <td>Dendrocygna</td>\n",
       "      <td>LC</td>\n",
       "      <td>47.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fulvous whistling-duck</td>\n",
       "      <td>Dendrocygna bicolor</td>\n",
       "      <td>Ducks/Geese/Waterfowl</td>\n",
       "      <td>Anseriformes</td>\n",
       "      <td>Anatidae</td>\n",
       "      <td>Dendrocygna</td>\n",
       "      <td>LC</td>\n",
       "      <td>45.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Snow goose</td>\n",
       "      <td>Anser caerulescens</td>\n",
       "      <td>Ducks/Geese/Waterfowl</td>\n",
       "      <td>Anseriformes</td>\n",
       "      <td>Anatidae</td>\n",
       "      <td>Anser</td>\n",
       "      <td>LC</td>\n",
       "      <td>64.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ross's goose</td>\n",
       "      <td>Anser rossii</td>\n",
       "      <td>Ducks/Geese/Waterfowl</td>\n",
       "      <td>Anseriformes</td>\n",
       "      <td>Anatidae</td>\n",
       "      <td>Anser</td>\n",
       "      <td>LC</td>\n",
       "      <td>57.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Greater white-fronted goose</td>\n",
       "      <td>Anser albifrons</td>\n",
       "      <td>Ducks/Geese/Waterfowl</td>\n",
       "      <td>Anseriformes</td>\n",
       "      <td>Anatidae</td>\n",
       "      <td>Anser</td>\n",
       "      <td>LC</td>\n",
       "      <td>64.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>3310.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Show a histogram of the MaxBodyMass data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "birds['MaxBodyMass'].plot(kind = 'hist',bins = 10,figsize = (12,12))\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Experiment with bins "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "birds['MaxBodyMass'].plot(kind = 'hist',bins = 30,figsize = (12,12))\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Filter the data and create a new histogram"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)]      \n",
    "filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12))\n",
    "plt.show()        \n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Create a 2D histgram showing the relationship between MaxBodyMass and MaxLength"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "x = filteredBirds['MaxBodyMass']\n",
    "y = filteredBirds['MaxLength']\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "hist = ax.hist2d(x, y)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Working with the filtered dataset, create a labelled and stacked histogram superimposing ConservationStatus with MaxBodyMass"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "x1 = filteredBirds.loc[filteredBirds.ConservationStatus=='EX', 'MinWingspan']\n",
    "x2 = filteredBirds.loc[filteredBirds.ConservationStatus=='CR', 'MinWingspan']\n",
    "x3 = filteredBirds.loc[filteredBirds.ConservationStatus=='EN', 'MinWingspan']\n",
    "x4 = filteredBirds.loc[filteredBirds.ConservationStatus=='NT', 'MinWingspan']\n",
    "x5 = filteredBirds.loc[filteredBirds.ConservationStatus=='VU', 'MinWingspan']\n",
    "x6 = filteredBirds.loc[filteredBirds.ConservationStatus=='LC', 'MinWingspan']\n",
    "\n",
    "kwargs = dict(alpha=0.5, bins=20)\n",
    "\n",
    "plt.hist(x1, **kwargs, color='red', label='Extinct')\n",
    "plt.hist(x2, **kwargs, color='orange', label='Critically Endangered')\n",
    "plt.hist(x3, **kwargs, color='yellow', label='Endangered')\n",
    "plt.hist(x4, **kwargs, color='green', label='Near Threatened')\n",
    "plt.hist(x5, **kwargs, color='blue', label='Vulnerable')\n",
    "plt.hist(x6, **kwargs, color='gray', label='Least Concern')\n",
    "\n",
    "plt.gca().set(title='Conservation Status', ylabel='Max Body Mass')\n",
    "plt.legend();"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Working with Seaborn, create a smooth plot about MinWingspan"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.kdeplot(filteredBirds['MinWingspan'])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Try a kdeplot about MaxBodyMass"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "sns.kdeplot(filteredBirds['MaxBodyMass'])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Experiment with the plot smoothing parameter"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Create a 2D kdeplot comparing MinLength and MaxLength with hue showing ConservationStatus"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "sns.kdeplot(data=filteredBirds, x=\"MinLength\", y=\"MaxLength\", hue=\"ConservationStatus\")\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/distributions.py:1078: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe4e053cf98>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ]
}